Traffic-monitoring cameras arranged along a road may be operated automatically to monitor traffic conditions. A typical traffic camera network includes cameras that are arranged along the length of the road and aimed to acquire video images of the road. Thus, the progress of a vehicle may be tracked as it travels along the road.
For example, automatic monitoring of the progress of individual vehicles as they travel along the road may be helpful in determining traffic conditions on the road. Timely detection of deviations from expected traffic patterns may enable prompt remedial action. For example, a traffic signal that is not operating properly may be reset, lost electrical power may be restored, police may be summoned to direct traffic, or a service vehicle may be summoned to remove a stalled vehicle from traffic.
Calibration may refer to a process of finding intrinsic and extrinsic parameters of a camera, such as a 3×4 camera matrix in the form of a homography. The camera matrix may be used to denote a projective mapping from world coordinates (e.g., x, y, z) to screen coordinates (e.g., u, v). Calibration may be performed for a world coordinate system, C, that is defined relative to the camera. To transform C into a global coordinate system W (e.g., latitude and longitude, or another global or regional coordinate system), an exact global location and orientation of the camera is required together with the obtained parameters of the calibration.
Many urban surveillance systems include a large number (e.g., thousands) of cameras. The cameras were neither calibrated during deployment, nor were their positions and orientations accurately measured. Instead, the cameras may be roughly associated with the name of the street being monitored or with rough Global Positioning System (GPS) estimates. Performance of a standard calibration process for each of the cameras may represent a tedious, expensive, and daunting task.